The present invention relates generally to vehicles, e.g., locomotives, electric or hybrid buses, etc., having resistive grids for dissipating substantial electrical power, and, more particularly, to a system and method for predicting impending failures in the resistive grids of the vehicle.
The description below is given for purposes of illustration and not of limitation in the context of a locomotive. As will be appreciated by those skilled in the art, a locomotive is a complex electromechanical system comprised of several complex subsystems. Each of these subsystems, such as the resistive grids, is built from components which over time fail. The ability to automatically predict failures before they occur in the locomotive subsystems is desirable for several reasons. For example, in the case of the power-dissipating resistive grids, that ability is important for reducing the occurrence of primary failures which result in stoppage of cargo and passenger transportation. These failures can be very expensive in terms of lost revenue due to delayed cargo delivery, lost productivity of passengers, other trains delayed due to the failed one, and expensive on-site repair of the failed locomotive. Further, some of those primary failures could result in secondary failures that in turn damage other subsystems and/or components. It will be further appreciated that the ability to predict failures before they occur in the resistive grids would allow for conducting condition-based maintenance, that is, maintenance conveniently scheduled at the most appropriate time based on statistically and probabilistically meaningful information, as opposed to maintenance performed regardless of the actual condition of the subsystems, such as would be the case if the maintenance is routinely performed independently of whether the subsystem actually needs the maintenance or not. Needless to say, a condition-based maintenance is believed to result in a more economically efficient operation and maintenance of the locomotive due to substantially large savings in cost. Further, such type of proactive and high-quality maintenance will create an immeasurable, but very real, good will generated due to increased customer satisfaction. For example, each customer is likely to experience improved transportation and maintenance operations that are even more efficiently and reliably conducted while keeping costs affordable since a condition-based maintenance of the locomotive will simultaneously result in lowering maintenance cost and improving locomotive reliability.
In one example of a diesel locomotive that may be propelled by DC traction motors, a pair of traction motors may be connected in parallel, and the resistive grid is connected in series between them. When the locomotive is motoring, the voltage drop across each traction motor is similar in magnitude and polarity, and as such, there is an insignificant voltage drop across the resistive grid. While using dynamic braking, however, the polarity of one of the traction motors is reversed, creating a substantial voltage drop across the resistive grid. Thus, in this example, the resistive grid is in constant electrical contact with the traction motors, yet dissipates energy only when the locomotive employs the dynamic braking technique, or when the resistive grid is used to test load the power alternator of the locomotive during a self-load mode of operation during which the traction motors are effectively disconnected and thus the self-load testing mode is performed without moving the locomotive. It will be appreciated that the foregoing modes of operation, i.e., dynamic braking and self-load testing, are not limited to locomotives using DC traction motors since such modes of operation are also typically available in locomotives using AC traction motors.
Problems may arise in the dynamic braking subsystem, however, when a resistive grid develops a short circuit to ground or to another element of the grid. Because, in some locomotives, the resistive grids may be permanently coupled to the traction motors, a short circuit can interrupt the current flow to the traction motors. Consequently, the locomotive can be completely disabled by an element that isn""t even involved in the actual propulsion of the locomotive. For example, short circuits may develop due to insulation loss in the resistive grid, or be caused by inadvertent damage during maintenance procedures. The likelihood of short circuits is enhanced by the fact that the grids are folded accordion style and packed tightly together to maximize heat transfer per area. When a short develops, the massive amount of electrical energy generated to propel the locomotive is diverted to ground. Attempting to operate a locomotive with a grounded resistive grid may cause damage to the locomotive""s electrical generation and propulsion systems, as well as the resistive grid itself. Thus, a locomotive with a grounded resistive grid may be disabled until the ground fault can be corrected. As suggested above, it is desired to develop a predictive diagnostic strategy that is suitable to predict incipient failures in the resistive grids.
Previous attempts to overcome the above-mentioned issues have been generally limited to diagnostics after a problem has occurred, as opposed to prognostics, that is, predicting a failure prior to its occurrence. For example, previous attempts to diagnose problems occurring in a locomotive have been performed by experienced personnel who have in-depth individual training and experience in working with locomotives. Typically, these experienced individuals use available information that has been recorded in a log. Looking through the log, the experienced individuals use their accumulated experience and training in mapping incidents occurring in locomotive subsystems to problems that may be causing the incidents. If the incident-problem scenario is simple, then this approach works fairly well for diagnosing problems. However, if the incident-problem scenario is complex, then it is very difficult to diagnose and correct any failures associated with the incident and much less to prognosticate the problems before they occur.
Presently, some computer-based systems are being used to automatically diagnose problems in a locomotive in order to overcome some of the disadvantages associated with completely relying on experienced personnel. Once again, the emphasis on such computer-based systems is to diagnose problems upon their occurrence, as opposed to prognosticating the problems before they occur. Typically, such computer-based systems have utilized a mapping between the observed symptoms of the failures and the equipment problems using techniques such as a table look up, a symptom-problem matrix, and production rules. These techniques may work well for simplified systems having simple mappings between symptoms and problems. However, complex equipment and process diagnostics seldom have simple correspondences between the symptoms and the problems. Unfortunately, as suggested above, the usefulness of these techniques have been generally limited to diagnostics and thus even such computer-based systems have not been able to provide any effective solution to being able to predict failures before they occur.
In view of the above-mentioned considerations, there is a general need to be able to quickly and efficiently prognosticate any failures before such failures occur in the fuel delivery subsystem of the locomotive, while minimizing the need for human interaction and optimizing the repair and maintenance needs of the subsystem so as to able to take corrective action before any actual failure occurs.
Generally speaking, the present invention fulfills the foregoing needs by providing a method for predicting failures in a respective resistive grid in a locomotive. The method allows for monitoring one or more signals indicative of an estimated grid resistance value. The method further allows for adjusting the value of the estimated grid resistance relative to a nominal grid resistance value. A comparing step allows for comparing the adjusted value of the grid resistance against the nominal grid resistance value to determine the performance of the respective resistive grid.
The present invention further fulfills the foregoing needs by providing a system for predicting failures in a respective grid of power resistors in a locomotive. The system includes a signal monitor configured to monitor respective signals indicative of an estimated grid resistance value. A first module is coupled to receive the value of the estimated grid resistance for adjusting the received grid resistance value relative to a nominal grid resistance value so as to generate an adjusted grid resistance value. A second module is coupled to receive the adjusted grid resistance value for comparing that value against the nominal grid resistance value so as to determine the performance of the respective resistor grid.